Using the Scan Tool Nickel Metal Hydride (NiMH) SOC% Data for Diagnostics

​Using the Scan Tool State-of-Charge (SOC) PID has long been a staple for technicians to use as diagnostic data.  The OEMs also have used it as a metric for determining the State-of-Health (SOH) of a Battery Pack (whether NiMH or Lithium).  Unfortunately, NiMH battery controllers induce errors in the SOC% as a function of time and significant errors can result.  These resulting errors can mislead a technician when evaluating the SOC or (more importantly) the SOH.  
The operation in a hybrid electric vehicle (HEV) is significantly dependent upon the SOH of the Battery Pack.  Fuel Economy and vehicle performance are tied directly to Battery Pack SOH (and performance).  
  



MYTH: The scan tool PID for the SOC% (capacity) of a Hybrid Electric Vehicle (HEV) Nickel Metal Hydride battery module or pack can be trusted for accuracy on a used vehicle to determine actual capacity.

FACT: The scan tool SOC% value on an aging HEV NiMH pack (e.g. starting at > 6 yrs in service) reflects what the battery controller “thinks” the state of charge is — not the actual SOC%. On a used vehicle, the two numbers often diverge by 10%, 20%, or more as the Battery Pack age increases. A technician who treats the PID as empirical will misdiagnose pack condition every time.



Why the SOC% PID Drifts From Reality
HEV battery controllers estimate SOC% using a combination of Coulomb counting, voltage-based lookup tables, and equivalent circuit models — all of which are calibrated against the assumptions of a new pack. As the pack ages, the physical properties of those models depend on shift, but the model does not. The result is a PID value that looks precise to three decimal places while being fundamentally wrong.
Coulomb Counting and the Fixed-Denominator Problem
Most HEV controllers integrate current flowing in and out of the pack over time, then divide by an assumed total capacity. As cells age and lose active material, actual pack capacity can fade by 20% or more — but the controller’s denominator doesn’t change. The SOC% calculation is therefore referenced against a capacity the pack no longer has. The PID overestimates remaining charge even when the math inside the controller is perfectly correct, because the number the math is divided by is wrong.

Progressive Cell Imbalance
HEV NiMH modules are series-connected. Manufacturing tolerances, thermal gradients across the pack, and uneven load exposure cause individual cells to age at different rates. A single weak cell will hit empty before the others are depleted, effectively stranding usable energy in the remaining cells. The controller, which typically monitors pack-level voltage rather than individual cell voltage, reports an average SOC% that tells the technician nothing about which cells are actually limiting the pack.

Internal Resistance and Voltage Sag
Internal resistance in an aging NiMH cell can climb to roughly 160% of its original value by end-of-life, driven primarily by corrosion of the negative metal hydride electrode. Under the high-current loads typical of HEV operation — regenerative braking, hard acceleration — this elevated resistance produces terminal voltage sag that the controller reads through its voltage-based lookup tables as low state of charge. The charge is actually there; the resistance is hiding it. The PID reports what the voltage says, not what the electrochemistry says.

Hysteresis and Nonlinear Aging
NiMH chemistry exhibits pronounced OCV hysteresis — open-circuit voltage at a given SOC differs by tens of millivolts depending on whether the pack was last charging or discharging, and this gap relaxes slowly over minutes to hours. Equivalent circuit models built into production HEV controllers simplify this behavior with static lookup tables. Those tables are accurate on a new pack at a reference temperature. On an aged pack operating across the thermal range a vehicle actually sees, the assumptions break down and the reported SOC% drifts accordingly.

Sensor Drift and Error Accumulation
Current and temperature sensors feeding the Coulomb counter carry small, persistent bias errors. Over thousands of charge and discharge cycles, these errors accumulate. HEV packs rarely see a full charge or full discharge — the controller operates in a narrow middle band — so the drift is seldom reset. After years of service, the cumulative error alone can render the PID value unreliable.



Key Takeaways
A capacity verdict based on scan tool SOC% is a guess dressed up as data. Genuine pack evaluation requires module-level voltage measurement under load, internal resistance testing per module, capacity verification with a reference load or stress test, and thermal imaging during a controlled discharge (if possible).  Customer complaints tied to SOC% errors are poor fuel economy, poor performance on acceleration, and shuddering during acceleration or high load conditions.  Examples of SOC% errors are when the Scan Tool provides an SOC% OF 61%, while the actual SOC% is less than 35% after being tested with off-board discharging equipment.  Since the SOC% represents stored energy, this type of error would significantly effect vehicle performance.   The scan tool SOC% PID is a control signal for the vehicle, not a diagnostic verdict for the technician. On a newer pack (i.e., <5 yrs) it is close enough to reality to be useful; an aged Battery Pack the SOC% error can be significant. Properly trained technicians verify SOC or SOH with instruments and validated testing processes, not assumptions.
Diagnostic and testing techniques are covered across the EV Pro+ L1 through L8 curriculum, along with the diagnostic tool competencies that separate a technician who can read a PID from one who can evaluate a Battery Pack.


Contact Us
If you would like to discuss HEV / EV battery diagnostics or technician training, contact us at:
📩 [email protected]
We welcome technical discussion.


Technical References
SAE International — Standards and Technical Articles
SAE J1798 (2019). Recommended Practice for Performance Rating of Electric Vehicle Battery Modules. SAE International, Battery Standards Testing Committee.
SAE J2288 (2008, reaffirmed). Life Cycle Testing of Electric Vehicle Battery Modules. SAE International.
SAE J1715 (2014). Hybrid Electric Vehicle (HEV) and Electric Vehicle (EV) Terminology. SAE International.
Andrushchak, V. (2025, Nov 17). Reducing SoC and SoH estimation errors: challenges and solutions in modern BMS. SAE International. sae.org/articles/2025/11/reducing-soc-soh-estimation-errors.

Peer-Reviewed Literature
Verbrugge, M., & Tate, E. (2004). Adaptive state of charge algorithm for nickel metal hydride batteries including hysteresis phenomena. Journal of Power Sources, 126(1–2), 236–249. (GM R&D; foundational SOC algorithm used in GM HEV programs.)
Pan, Y.H., Srinivasan, V., & Wang, C.Y. (2002). An experimental and modeling study of isothermal charge/discharge behavior of commercial Ni-MH cells. Journal of Power Sources, 112(2), 298–306.
Ota, H., et al. (2011). Modeling of voltage hysteresis and relaxation of HEV NiMH battery. Electrical Engineering in Japan, Wiley.
Wu, B., & White, R.E. (2001). Modeling of a nickel-hydrogen cell phase reaction in the nickel active material. Journal of The Electrochemical Society, 148(6), A595–A609.
Roscher, M.A., et al. (2011). OCV Hysteresis in Li-Ion Batteries including Two-Phase Transition Materials. International Journal of Electrochemistry, Wiley Online Library.

MDPI Open-Access Journals
Bertilsson, S., et al. (2021). Short-Term Impact of AC Harmonics on Aging of NiMH Batteries for Grid Storage Applications. Batteries, MDPI. (Identifies negative-electrode corrosion as the primary NiMH aging mechanism.)
Zhang, S., et al. (2023). Capacity Degradation and Aging Mechanisms Evolution of Batteries under Different Operation Conditions. Energies, 16(10), 4232. MDPI.
Madani, S.S., et al. (2025). A Comprehensive Review on Battery Lifetime Prediction and Aging Mechanism Analysis. Batteries, 11(4), 127. MDPI.
U.S. Department of Energy / National Laboratory Sources
Motloch, C.G., et al. (2002). Implications of NiMH Hysteresis on HEV Battery Testing and Performance. Proceedings of the 19th International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium (EVS-19). Idaho National Engineering and Environmental Laboratory.
PNGV Battery Test Manual (2001). DOE/ID-10597, Revision 3. U.S. Department of Energy, Partnership for a New Generation of Vehicles.

IEEE Conference Proceedings
Battery Pack Inconsistency Modeling and SOC Estimation Based on Improved Mean-Difference Model (2025). Proceedings of the 2025 37th Chinese Control and Decision Conference (CCDC), 16–19 May 2025. IEEE Xplore.

Industry and Engineering References
Battery Design LLC. SoC Estimation by Coulomb Counting. batterydesign.net technical reference library.
Tang, X., Zhang, X., Koch, B., & Frisch, D. (2008). Modeling and estimation of nickel metal hydride battery hysteresis for SOC estimation. IEEE Prognostics and Health Management Conference Proceedings, 1–12.

Disclaimer: 
This content is provided for general informational purposes only. It is based on publicly available data, standards, and published sources available at the time of release. It does not constitute advice of any kind. Information is provided as-is, without warranties, and no liability is assumed for actions taken based on this content.

​

---